Gaze estimation for off-angle iris recognition based on the biometric eye model

Karakaya, Mahmut; Barstow, Del; Santos-Villalobos, Hector; Thompson, Joseph; Bolme, David S.; Boehnen, Christopher

doi:10.1117/12.2018614

Cited by 6 publications

(9 citation statements)

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“…In order to estimate the three-dimensional HPOM, the shape of the pupil, which is deformed into an elliptical shape according to the gaze direction, can be analyzed and converted into three-dimensional coordinates. The positional relationship between the ORCP and threedimensional the PCP can then be analyzed [10].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The pupil is observed as an ellipse according to the gaze direction. It can be assumed to be a circle with zero eccentricity when the camera is viewed from the front [10]. Based on this, the two prerequisites were set to estimate the 3D position of the PCP as follows:…”

Section: Ppc Analysismentioning

confidence: 99%

“…As methods of tracking the gaze close to the eye, a method using a smart lens that emits a laser and a method using the position of a glint created in the eye by attaching a near-infrared light source to glasses have been reported [6,7]. The method of photographing the front of the face is being studied as a method of tracking the gaze by analyzing the shape of the pupil or iris through an image processing technique after photographing the face [8][9][10]. Further, research has been conducted on a method of tracking the gaze using a glint that reflects a light source on the iris [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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A study of Human pupil orbit model estimation method for the eye-gaze tracking.

Lee

Jeong

Kim

et al. 2022

Preprint

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Background: Eye tracking is a technology that detects the direction of the user's gaze in real time. This technology is used in various fields such as analyzing user emotions and behaviors by analyzing the length of time the gaze stays in a specific direction or the path of movement. Recently, Eye-gaze tracking is a popular to a non-contact interaction technique for the minimization of covid-19 infection. A study conducted in the past have a limitation in that the ocular rotational center point (ORCP) cannot be found in common. For this reason, each research is continuously researching to overcome this problem by using various sensors, structures, and phenomena. In spite of, a tiny movement of the subject cause an error in accuracy during the process of Eye-gaze tracking. In this paper, we propose a novel method for Eye-gaze tracking under the condition of photographing the front of the face using a camera. We named this position set as Human pupil orbit model (HPOM) and it can express as a spherical equation because it looks like a sphere in 3-dimensional coordinate.Results: A simulation experiment was performed to confirm the performance of the proposed method. The error of HPOM estimation appears under 1 pixel in the first experiment both ORCP and a radius. In addition, it was confirmed that the process of estimating HPOM can be conducted as a real time in the second experiment.Conclusions: The proposed method was able to overcome the common problem of existing studies that could not find the rotational center of an eye-ball. In addition, HPOM may be secured in real time even if the subject moves.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Ppc Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A study of Human pupil orbit model estimation method for the eye-gaze tracking.

Lee

Jeong

Kim

et al. 2022

Preprint

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“…The algorithm utilises a brute-force technique to re-project the input image to all possible gaze angles. In [31], the authors utilised the boundary segmentation of the iris and pupil and compared the boundaries with a look-up table generated by using a biologically inspired biometric eye model and finding the closest feature point in the look-up table to estimate the gaze angles. Bolme et al [32] presented four methods for correcting off-angle iris images so that they appear frontal.…”

Section: Related Workmentioning

confidence: 99%

CNN‐based off‐angle iris segmentation and recognition

2021

Self Cite

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Accurate segmentation and parameterisation of the iris in eye images still remain a significant challenge for achieving robust iris recognition, especially in off-angle images captured in less constrained environments. While deep learning techniques (i.e. segmentation-based convolutional neural networks (CNNs)) are increasingly being used to address this problem, there is a significant lack of information about the mechanism of the related distortions affecting the performance of these networks and no comprehensive recognition framework is dedicated, in particular, to off-angle iris recognition using such modules. In this work, the general effect of different gaze angles on ocular biometrics is discussed, and the findings are then related to the CNN-based off-angle iris segmentation results and the subsequent recognition performance. An improvement scheme is also introduced to compensate for some segmentation degradations caused by the off-angle distortions, and a new gaze-angle estimation and parameterisation module is further proposed to estimate and re-project (correct) the offangle iris images back to frontal view. Taking benefit of these, several approaches (pipelines) are formulated to configure an end-to-end framework for the CNN-based offangle iris segmentation and recognition. Within the framework of these approaches, a series of experiments is carried out to determine whether (i) improving the segmentation outputs and/or correcting the output iris images before or after the segmentation can compensate for some off-angle distortions, (ii) a CNN trained on frontal eye images is capable of detecting and extracting the learnt features on the corrected images, or (iii) the generalisation capability of the network can be improved by training it on iris images of different gaze angles. Finally, the recognition performance of the selected approach is compared against some state-of-the-art off-angle iris recognition algorithms.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Schuckers et al [29] noted considerable improvement on iris recognition performance when gaze angle was compensated for images which are up to 15 o off-axis. Also, current gaze estimation techniques can estimate gaze angle with an average 3.5 o error over a 50 o range [30].…”

Section: ) Usable Iris Areamentioning

confidence: 99%